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General framework for eco-coevolutionary transitions from antagonism to mutualismWe develop a general framework in which we model interactions between host species i (density Hi) and its partner species k (density Fk), which are initially purely antagonistic. The model is general, but could be applied broadly to bacterial hosts and parasitic phages or plant hosts and animal or fungal partners, for example. The ecological dynamics of this community (without evolution) are given by:$$frac{d{H}_{i}}{{dt}}={g}_{i}{H}_{i}left(1-mathop{sum}limits_{j}{q}_{{ij}}{H}_{j}right)+{sum }_{k}{f}!_{ik}left[beta left({H}_{i},{F}_{k}right),alpha left({{H}_{i},F}_{k}right)right]$$
(4a)
$$frac{d{F}_{k}}{{dt}}=mathop{sum}limits_{i}{f}_{ki}left[beta left({H}_{i},{F}_{k}right),alpha left({{H}_{i},F}_{k}right)right]-{delta }_{k}{F}_{k}$$
(4b)
The first term of Eq. (4a) describes host population growth in the absence of partner species, where gi is its intrinsic per capita growth rate and qij is the competitive effect of host j on host i for other limiting factors. The general function fik describes the effects of interactions with partner k on host i: β(Hi, Fk) gives the potential mutualism and α(Hi, Fk) describes the antagonism. In Eq. (4b), the general function fki gives the effects of interactions with host i on partner k and δk is the partner’s per capita mortality rate.To derive an explicit eco-coevolutionary model, we apply Equation (4) to model interactions between a single host species and its exclusive partner species (for the sake of simplicity) in terms of host traits xi and partner traits yi (involved in interactions with host i); the ecological dynamics of which are given by:$$frac{1}{{H}_{i}}frac{d{H}_{i}}{{dt}}={g}_{i}left(1-{q}_{i}{H}_{i}right)+frac{bleft[{x}_{i}^{B}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]{F}_{k}}{{S}_{i}+vleft[{x}_{i}^{V},{y}_{i}^{V}right]{F}_{k}}-hleft[{x}_{i}^{H},{y}_{i}^{H}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]{F}_{k}$$
(5a)
$$frac{1}{{F}_{k}}frac{d{F}_{k}}{{dt}}=eleft[{y}_{i}^{V},{y}_{i}^{H}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]hleft[{x}_{i}^{H},{y}_{i}^{H}right]{H}_{i}-{delta }_{k}$$
(5b)
where b is the mutualistic benefits to the host, v is the visitation rate, Si is a saturation constant, h is the costs of antagonism to the host and its benefits to the partner, and e is the partner’s conversion efficiency. The mutualistic and antagonistic interactions are assumed to contribute additively to host population growth and multiplicatively to partner population growth, assumptions that may be valid for many types of interactions, but will not apply universally. To prevent unbounded population growth in the model, the effects of mutualism on host population growth are assumed to saturates with increasing partner density.The function b[xiB] gives the mutualistic benefits of the partner as a function of host trait xiB:$$bleft[{x}_{i}^{B}right]={b}_{{max },i}left(frac{2}{1+{e}^{-{B}_{i}^{{prime} }{x}_{i}^{B}}}-1right)$$
(6a)
where bmax,i gives the maximum mutualistic benefits and ({B}_{i}^{{prime} }) is a saturation constant. The interaction is purely antagonistic when xiB = 0. As xiB increases, the mutualistic benefits b[xiB] increase towards bmax,i.The function v[xiV, yiV] gives visitation rate as a sigmoid function of host trait xiV and partner trait yiV:$$vleft[{x}_{i}^{V},{y}_{i}^{V}right]=frac{{v}_{{max },i}}{1+{e}^{-{V}_{i}^{{prime} }left({x}_{i}^{V}+{y}_{i}^{V}right)}}$$
(6b)
where vmax,i is the maximum visitation rate and ({V}_{i}^{{prime} }) determines how rapidly visitation rate changes as host and partner traits change. As xiV or yiV increase, the visitation rate increases and approaches vmax,i when xiV + yiV → ∞. As xiV or yiV decrease, the visitation rate decreases and approaches zero when xiV + yiV → −∞. Negative values of xiV indicate that the host species is reducing its attraction of the partner species.The function h[xiH, yiH] gives the costs of antagonism to the host and its benefits to the partner, which is described via a sigmoid function of the difference between host trait xiH and partner trait yiH:$$hleft[{x}_{i}^{H},{y}_{i}^{H}right]=frac{{h}_{{max },i}}{1+{e}^{{H}_{i}^{{prime} }left({x}_{i}^{H}-{y}_{i}^{H}right)}}$$
(6c)
where hmax,i gives the maximum antagonism and ({H}_{i}^{{prime} }) determines how antagonism changes as the difference between host and partner traits increases. When xiH > yiH, antagonism declines and approaches zero when xiH – yiH → ∞, while when xiH < yiH, antagonism increases and approaches hmax,i when xiH – yiH → -∞ (unlike xiV, xiH cannot be negative).Partner traits yiV and yiH trade off with conversion efficiency via the function e[yiV, yiH] as defined by:$$eleft[{y}_{i}^{V},{y}_{i}^{H}right]={e}_{{max },i}{e}^{-left({c}_{I,i}^{V}{left({y}_{i}^{V}right)}^{2}+{c}_{I,i}^{H}{left({y}_{i}^{H}right)}^{2}right)}$$ (6d) where emax,i is the maximum conversion efficiency when interacting with host i (when yiV = yiH = 0), and cI,iV and cI,iH determine how rapidly conversion efficiency declines as yiV or yiH increase, thus quantifying the costliness of traits yiV and yiH, respectively. This trade-off shape was chosen because it is unimodal and constrains conversion efficiency to always be positive. Host trade-offs are defined below (Eq. 8c).Host-partner coevolutionary dynamicsWe model coevolution via the adaptive dynamics framework17,18. Coevolution of a mutant host trait ximut and partner trait yimut (for any general traits xi and yi) is given by:$$frac{d{{x}_{i}}^{{mut}}}{dtau }={mu }_{x}{left.frac{partial {W}_{H}left({{x}_{i}}^{{mut}},{x}_{i},{y}_{i}right)}{partial {{x}_{i}}^{{mut}}}right|}_{{{x}_{i}}^{{mut}}={x}_{i}}$$ (7a) $$frac{d{{y}_{i}}^{{mut}}}{dtau }={mu }_{y}{left.frac{partial {W}_{F}left({{y}_{i}}^{{mut}},{y}_{i},{x}_{i}right)}{partial {{y}_{i}}^{{mut}}}right|}_{{{y}_{i}}^{{mut}}={y}_{i}}$$ (7b) where τ is the evolutionary timescale, μx and μy give, respectively, the rates of host and partner evolution, and WH(ximut,xi,yi) and WF(yimut,yi,xi) are the invasion fitness (per capita growth rate when rare) of a mutant host and partner species with trait ximut and yimut in a resident community with trait xi and yi, respectively. The partial derivatives ({left.partial {W}_{H}/partial {{x}_{i}}^{{mut}}right|}_{{{x}_{i}}^{{mut}}={x}_{i}}) and ({left.partial {W}_{F}/partial {{y}_{i}}^{{mut}}right|}_{{{y}_{i}}^{{mut}}={y}_{i}}) are the selection gradients.We model coevolution of mutualistic benefits from the focal partner species (via b), attraction (via v), and defense (via h). The invasion fitness of the mutant host and a mutant partner are given by:$${W}_{H}={g}_{i}left(1-qleft[{{x}_{i}}^{{mut}},{x}_{i}right]{{H}_{i}}^{ast }right)+frac{bleft[{x}_{i}^{B,{mut}}right]vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{{F}_{k}}^{ast }}{{S}_{i}+vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{{F}_{k}}^{ast }}-hleft[{x}_{i}^{H,{mut}},{y}_{i}^{H}right]{vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{F}_{k}}^{ast }$$ (8a) $${W}_{F}=eleft[{y}_{i}^{V,{mut}},{y}_{i}^{H,{mut}}right]vleft[{x}_{i}^{V},{y}_{i}^{V,{mut}}right]hleft[{x}_{i}^{H},{y}_{i}^{H,{mut}}right]{{H}_{i}}^{ast }-{delta }_{k}$$ (8b) where Hi* and Fk* are species’ densities at the ecological equilibrium (of Eq. 5). The functions b, v, h, and e are given by Eq. (6a–d), respectively, where xi and yi are replaced with ximut in Eq. (8a) and yimut in Eq. (8b). The function q[ximut,xi] describes trade-offs between mutant host traits and mutant host competitive ability as defined by:$$qleft[{{x}_{i}}^{{mut}},{x}_{i}right]=1+{c}_{H,i}^{B}left({left({{x}_{i}}^{B,{mut}}right)}^{{s}_{i}^{B}}-{left({{x}_{i}}^{B}right)}^{{s}_{i}^{B}}right)+{c}_{H,i}^{V}left({left({{x}_{i}}^{V,{mut}}right)}^{{s}_{i}^{V}}-{left({{x}_{i}}^{V}right)}^{{s}_{i}^{V}}right)+{c}_{H,i}^{H}left({left({{x}_{i}}^{H,{mut}}right)}^{{s}_{i}^{H}}-{left({{x}_{i}}^{H}right)}^{{s}_{i}^{H}}right)$$ (8c) If ximut > xi for any trait, the competitive effect experienced by the mutant host is increased by an amount taken to be proportional (for simplicity) to the difference between the trait values, ximut – xi, whereas if ximut < xi, the competitive effect experienced by the mutant host is decreased by that amount. The coefficients cH,iB, cH,iV, and cH,iH measure the costs associated with the trade-off for each trait, while the shape parameters siB, siV, and siH define whether the trade-offs are linear (si = 1), concave (si < 1), or convex (si > 1).Mutualism can evolve via the COA for all trade-off shapes (Supplementary Fig. 3). Parameter space plots show that the interaction transitions from antagonism to net mutualism when the costs associated with host traits underlying attraction (cH,iV) and defense (cH,iH) are within a range beyond which there is evolutionary purging of the partner (Supplementary Fig. 3a–c). Only with convex trade-offs can the net antagonism persist. The coevolution of mutualism also requires that the costs associated with partner traits underlying visitation (cFV) and antagonism (cFH) exceed a threshold (Supplementary Fig. 3d–f) below which there is evolutionary purging of the partner (linear or convex trade-offs) or the net antagonism persists (linear or concave trade-offs). Coevolution of mutualism occurs across greater parameter ranges when the trade-offs are linear or slightly concave because costs increase less rapidly than with convex trade-offs.Ecological model of plant-insect interactionsWe tailor the general model (Eq. 4) to model populations of D. wrightii (density Pw) and D. discolor (density Pd) interacting with M. sexta. We scale the model so that Pi = 1 in the absence of M. sexta: thus, Pi >1 indicates that pollination benefits exceed herbivory costs, and Pi < 1 indicates that herbivory costs exceed pollination benefits. The Datura species do not rely obligately on M. sexta and, consistent with ecology of the natural community (Box 1), the model incorporates the alternative host plant, Proboscidea parviflora (density Pp), and the alternative nectar source, Agave palmeri. The ecological dynamics of this community (without evolution) are given by:$$frac{1}{{P}_{i}}frac{d{P}_{i}}{{dt}}=left(1-{P}_{i}right)+frac{{b}_{i}{v}_{i}A}{H+{v}_{i}A}-{h}_{i}{L}_{i}$$ (9a) $$frac{d{L}_{i}}{{dt}}=varepsilon {e}_{i}{v}_{i}{P}_{i}A-{m}_{i}{h}_{i}{L}_{i}-{d}_{i}{L}_{i}$$ (9b) $$frac{{dA}}{{dt}}=mathop{sum}limits_{i}{rho }_{i}{m}_{i}{h}_{i}{L}_{i}-{d}_{A}A$$ (9c) Equation (9a) describes the population dynamics of plant species i (D. wrightii, D. discolor, or P. parviflora). Equation (9b,c) give the dynamics of M. sexta: Li gives the larvae density on plant species i, which recruit into the adult population, A. Pollination is described by the term biviA/(H+viA), where bi is the per capita growth of plant species i due to pollination by the antagonist, vi is the visitation rate to plant species i per antagonist adult, and H is the saturation constant for pollination. Oviposition is given by εeiviPiA, where ei is the oviposition efficiency (number of eggs laid per floral visit) and ε is the fractional increase in egg production due to nectar-feeding at A. palmeri. Floral visits lead to both pollination and oviposition because these behaviors have been shown to be tightly linked in M. sexta19. Pollination and oviposition are given by saturating and linear functions, respectively, based on our data (Supplementary Data 1). Herbivory damage is given by the term hiLi, where hi is the herbivory rate per larvae on plant species i. Larvae mature at rate mihiLi, where mi is the maturation efficiency (fraction of larvae maturing on plant species i). Larval mortality on plant species i is di, adult mortality is dA, and ρi is pupae survival (due to data constraints, we include pupae survival in our estimates of maturation mi, set ρi = 1, and drop ρi from equations hereafter). Equation (9a) gives the dynamics of the alternative larval host plant, P. parviflora (bp = 0 and cannot evolve), which can coevolve attraction and defense. The alternative nectar source, A. palmeri, is incorporated within the model via the parameter ε.Model scalingWithout the antagonist, plant population growth is given by gi (1 – qiPi), where gi is the per capita growth rate of plant species i due to autonomous self-pollination or pollination by other species and qi is plant self-limitation. As qi is very difficult to quantify in nature, we scale the model so that Pi = 1 without the antagonist. We scale plant density ((hat{{P}_{i}}={q}_{i}{P}_{i})), larvae density ((hat{{L}_{i}}={q}_{i}{L}_{i})), herbivory rate ((hat{{h}_{i}}={h}_{i}/{q}_{i})), maturation efficiency ((hat{{m}_{i}}={q}_{i}{m}_{i})), and survival of pupae ((hat{{rho }_{i}}={rho }_{i}/{q}_{i})); where the hats denote scaled quantities and are dropped elsewhere for clarity. Thus, the model is scaled for parameterization, but is not non-dimensionalized. We then scale gi to 1 such that pollination benefits, bi, are estimated by the ratio of the seed set of moth-pollinated flowers to autonomously self-pollinated flowers. Parameter estimates are for scaled quantities.Interaction breakdown boundary for ancestral interaction in a one-plant species communityFor the ancestral insect to persist, its per capita growth rate must be positive when it is rare (i.e., at Pi* = 1, Li* = 0, A* = 0). In stage-structured models, the per capita growth rate is given by the dominant eigenvalue (λD) of the matrix:$$left[begin{array}{cc}-{m}_{i}{h}_{i}-{d}_{i} & varepsilon {e}_{i}{v}_{i}{{P}_{i}}^*\ {m}_{i}{h}_{i} & -{d}_{A}end{array}right]$$which is given by:$${lambda }_{D}=frac{1}{2}left(-{d}_{A}-{d}_{i}-{m}_{i}{h}_{i}+sqrt{{({d}_{A}+{d}_{i}+{m}_{i}{h}_{i})}^{2}-4({d}_{A}left({d}_{i}+{m}_{i}{h}_{i}right)-varepsilon {e}_{i}{v}_{i}{m}_{i}{h}_{i})}right).$$For the insect to persist, λD must have a positive real part, which occurs only when the second term in the square root of λD is negative; i.e., ({d}_{A}left({d}_{i}+{m}_{i}{h}_{i}right)-varepsilon {e}_{i}{v}_{i}{m}_{i}{h}_{i} , , 1). Applying ({f}_{i}=frac{varepsilon {e}_{i}{v}_{i}}{{d}_{A}}) and ({s}_{i}=frac{{m}_{i}{h}_{i}}{{{m}_{i}{h}_{i}+d}_{i}}), where fi is insect lifetime fecundity and si is the larval success (probability of larvae maturing rather than dying), yields Eq. (1).Interaction transition boundary in a one-plant species communityFor the interaction to transition from antagonism to mutualism, equilibrium plant density, Pi* must exceed one (see “Model scaling”). Setting Eq. (9b) to zero and solving for Pi* yields:({{P}_{i}}^{ast }=frac{{{m}_{i}{h}_{i}+d}_{i}}{varepsilon {e}_{i}{v}_{i}}left(frac{{{L}_{i}}^{ast }}{{A}^{ast }}right)). Setting Eq. (9c) to zero and rearranging terms then yields: (frac{{{L}_{i}}^{ast }}{{A}^{ast }}=frac{{d}_{A}}{{m}_{i}{h}_{i}}). Thus, ({{P}_{i}}^{ast }=frac{{{m}_{i}{h}_{i}+d}_{i}}{varepsilon {e}_{i}{v}_{i}}left(frac{{d}_{A}}{{m}_{i}{h}_{i}}right)) and (rearranging slightly) the condition for mutualism to arise is: ({{P}_{i}}^{ast }=left(frac{{d}_{A}}{varepsilon {e}_{i}{v}_{i}}right)left(frac{{{m}_{i}{h}_{i}+d}_{i}}{{m}_{i}{h}_{i}}right) , > , 1). Rearranging and applying ({f}_{i}=frac{varepsilon {e}_{i}{v}_{i}}{{d}_{A}}) and ({s}_{i}=frac{{m}_{i}{h}_{i}}{{{m}_{i}{h}_{i}+d}_{i}}) yields Eq. (2).Interaction breakdown boundary in a one-plant species communityIn the ancestral interaction, insect persistence is evaluated by whether or not it can increase from low density, which yields Eq. (1). Within the net mutualistic region, however, the insect cannot increase from very low density because it cannot buoy plant density sufficiently to maintain a positive per capita growth rate (mathematically, Eq. 1 cannot hold when Eq. 2 is satisfied). The mutualistic region is thus characterized by bistability (see Supplementary Figure 1), and the interaction breakdown boundary is determined by the conditions for the coexistence equilibrium to exist. At the coexistence equilibrium, the larval and adult densities are: ({{L}_{i}}^{ast }=frac{-B+sqrt{{B}^{2}-4{A}_{L}{C}_{L}}}{2{A}_{L}}) and ({A}^{ast }=frac{-B+sqrt{{B}^{2}-4{A}_{A}{C}_{A}}}{{2A}_{A}}), where ({A}_{L}=varepsilon {e}_{i}{{v}_{i}}^{2}{h}_{i}{d}_{A}), ({A}_{A}=varepsilon {e}_{i}{{v}_{i}}^{2}{m}_{i}{{h}_{i}}^{2}), (B=varepsilon {e}_{i}{{v}_{i}}^{2}{m}_{i}{h}_{i}left(frac{1}{{f}_{i}{s}_{i}}+frac{H}{{v}_{i}{m}_{i}}-left(1+{b}_{i}right)right)), ({C}_{L}=varepsilon {e}_{i}{v}_{i}H{d}_{A}left(frac{1}{{f}_{i}{s}_{i}}-1right)), and ({C}_{A}=varepsilon {e}_{i}{v}_{i}{m}_{i}{h}_{i}Hleft(frac{1}{{f}_{i}{s}_{i}}-1right)). For the coexistence equilibrium to exist, either CL and CA must be negative or B must be negative and Li* and A* must be real. CL and CA are negative when fi si > 1, which is Eq. (1) and cannot hold within the mutualistic region because Eq. (2) must be satisfied. However, B is negative when ({f}_{i}{s}_{i}left(left(1+{b}_{i}right)-frac{H}{{v}_{i}{m}_{i}}right) , > , 1), which is approximated by Eq. (3) when the last term is assumed to be small. For Li* and A* to be real, B2 – 4ALCL > 0 and B2 – 4AACA > 0. Assuming that the pollination saturation constant is small (i.e., H ≈ 0) yields CL ≈ CA ≈ 0 such that ({{L}_{i}}^{ast }approx frac{-B}{{A}_{L}}approx frac{{m}_{i}}{{d}_{A}{f}_{i}{s}_{i}}left({f}_{i}{s}_{i}left(1+{b}_{i}right)-1right)) and ({A}^{ast }approx frac{-B}{{A}_{A}}approx frac{1}{{h}_{i}}left({f}_{i}{s}_{i}left(1+{b}_{i}right)-1right)), which are both positive when fi si (1 + bi) > 1 as approximated by Eq. (3).Interaction transition and breakdown boundaries in a two-plant species communityThese boundaries are analytically intractable and are estimated by simulation (see codes provided online).Coevolutionary dynamics of plants and insectThe effects of plant traits xi and insect traits yi on the ecological dynamics of the interactions are given by:$$frac{1}{{P}_{i}}frac{d{P}_{i}}{{dt}}=left(1-{P}_{i}right)+frac{bleft[{x}_{i}^{B}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]A}{H+vleft[{x}_{i}^{V},{y}_{i}^{V}right]A}-hleft[{x}_{i}^{H},{y}_{i}^{H}right]{L}_{i}$$
(10a)
$$frac{d{L}_{i}}{{dt}}=varepsilon eleft[{y}_{i}^{V},{y}_{i}^{H}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]{P}_{i}A-{m}_{i}hleft[{x}_{i}^{H},{y}_{i}^{H}right]{L}_{i}-{d}_{i}{L}_{i}$$
(10b)
$$frac{{dA}}{{dt}}=mathop{sum}limits_{i}{m}_{i}hleft[{x}_{i}^{H},{y}_{i}^{H}right]{L}_{i}-{d}_{A}A$$
(10c)
We model coevolution of plant-insect interactions using the adaptive dynamics framework17,18 to link population dynamics and trait coevolution. The coevolution of mutant plant trait xmut and insect trait ymut (for general traits x and y) is given by Equation (7). We model the coevolution of pollination benefits from the antagonist, bi (via mutant plant trait xiB,mut), attraction (via mutant plant trait xiV,mut and mutant insect trait yiV,mut), and defense (via mutant plant trait xiH,mut and mutant insect trait yiH,mut). The invasion fitness of a mutant plant is given by:$${W}_{P,i}left({x}_{i}^{{mut}},{x}_{i},{y}_{i}right)=left(1-qleft[{x}_{i}^{{mut}},{x}_{i}right]{{P}_{i}}^{ast }right)+frac{bleft[{x}_{i}^{B,{mut}}right]vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{A}^{ast }}{H+vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{A}^{ast }}-hleft[{x}_{i}^{H,{mut}},{y}_{i}^{H}right]{{L}_{i}}^{ast }$$
(11a)
where Pi*, Li*, and A* are the densities of the plant, insect larvae per plant, and insect adults, respectively, at the ecological equilibrium (of Eq. 10). The functions (b[x_{i}^{B,mut}], v[x_{i}^{V,mut} , y_{i}^{V}],) and (h[x_{i}^{H,mut}, y_{i}^{H}]), describe the effects of mutant plant traits (x_{i}^{B,mut}), (x_{i}^{V,mut}), (x_{i}^{H,mut}), and (x_{i}^{H,mut}) on pollination benefits, attraction, and defense, respectively, which are defined by Eq. (6a–c), where xi is replaced with ximut (where the plant is the host species and the insect is the partner species). The function q[x,mut, xi] defines the trade-offs between mutant plant traits and the competitive ability of mutant plants, which is given by Eq. (8c) (with si = 1). At a coESS, ximut = xi for all traits such that q[ximut, xi] = 1 and the original definition of Pi >1 indicating that pollination benefits exceed herbivory costs is retained when pollination benefits evolve.Invasion fitness of a mutant insect is given by the dominant eigenvalue of its system of equations evaluated at the resident equilibrium. In a one-plant species community, the insect invasion fitness is:$${W}_{I,i}=frac{1}{2}left(-{d}_{A}-{d}_{i}-{m}_{i}{h}_{i}^{{mut}}+sqrt{{left({d}_{A}+{d}_{i}+{m}_{i}{h}_{i}^{{mut}}right)}^{2}-4left({d}_{A}left({d}_{i}+{m}_{i}{h}_{i}^{{mut}}right)-frac{varepsilon {e}_{i}^{{mut}}{v}_{i}^{{mut}}{h}_{i}^{{mut}}{d}_{A}left({d}_{i}+{m}_{i}{h}_{i}right)}{{e}_{i}{v}_{i}{h}_{i}}right)}right)$$
(11b)
where vimut, himut, and eimut are functions describing the effects of mutant insect traits on attraction, defense, and mutant oviposition efficiency, respectively, which are given by Eq. (6b–d), where yi is replaced with yimut. Invasion fitness of a mutant insect in a two-plant species community is given by the dominant eigenvalue of its system of equations evaluated at the resident equilibrium, which is analytically tractable, but sufficiently complicated that we do not include it here (see codes provided online).The curves where the selection gradients (see Eqs. 7) become zero give the evolutionary isoclines for the coevolutionary system. The points where the isoclines intersect give the coevolutionary singularities, which are coevolutionary stable states (coESSs) when they are stable for both plants and the insect. For tractability, the local stability of the coevolutionary singularities was assessed by carefully inspecting the selection gradient of each trait in the neighborhood of its coESS with all other traits held at their coESS as well as by simulating coevolutionary dynamics. Importantly, all three plant traits (xiB, xiV, and xiH) and both insect traits (yiV and yiH) all coevolve simultaneously in the model.Coevolution of the ancestral antagonistic interactionIn the ancestral interaction, pollination by the antagonist is impossible (bi = 0) and thus visitation only contributes to oviposition. From the plant perspective, the selection gradients for attraction and defense in the ancestral interaction are given by:$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{V,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=-{c}_{P,i}^{V}{{P}_{i}}^{ast }$$
(12a)
$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{H,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=frac{{h}_{{max },i}{H}_{i}^{{prime} }{e}^{{H}_{i}^{{prime} }left({x}_{i}^{H}-{y}_{i}^{H}right)}}{{left(1+{e}^{{H}_{i}^{{prime} }left({x}_{i}^{H}-{y}_{i}^{H}right)}right)}^{2}}{{L}_{i}}^{ast }-{c}_{P,i}^{H}{{P}_{i}}^{ast }$$
(12b)
Equation (12a) predicts that selection favors plant traits that reduce attracting the antagonist (e.g., reduced production of volatiles) and lower costs associated with competitive ability. We constrain xiV to be non-negative in the ancestral interaction so that xiV = 0 at the coESS; otherwise, xiV → –∞ and the plant always purges the insect given this model parameterization. Selection balances reduced herbivory damage (first term of Eq. 12b) with costs of reduced competitive ability (second term of Eq. 12b). Selection gradients for insect traits are sufficiently complicated that we do not include them here (see codes provided online); however, selection balances traits that increase visitation and overcome plant defenses with the costs associated with reduced oviposition. The ancestral coESSs are given in Supplementary Table 3.Coevolution of pollination benefits, attraction, and defenseThe evolution of mutant plant traits that allow the antagonist to pollinate it (bimut > 0) initiates the evolution of pollination benefits from the antagonist. The selection gradient for pollination benefits from the antagonist is given by:$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{B,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=frac{2{b}_{{max },i}{B}_{i}^{{prime} }{e}^{-{B}_{i}^{{prime} }{x}_{i}^{B}}vleft[{x}_{i}^{V},{y}_{i}^{V}right]{A}^{ast }}{{left(1+{e}^{-{B}_{i}^{{prime} }{x}_{i}^{B}}right)}^{2}left(H+vleft[{x}_{i}^{V},{y}_{i}^{V}right]{A}^{ast }right)}-{c}_{P,i}^{B}{{P}_{i}}^{ast }$$
(13a)
Equation (13a) shows that plants evolve traits to benefit from floral visits by the antagonist when selection for increased pollination benefits (first term of Eq. 13a) exceeds the costs associated with reduced competitive ability (second term of Eq. 13a).In the model, pollination benefits from the antagonist evolve via Eq. (13a) simultaneously with plant and insect traits affecting attraction and defense. The plant selection gradient for attraction is now:$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{V,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=frac{bleft[{x}_{i}^{B}right]{v}_{{max },i}{V}_{i}^{{prime} }{e}^{-{V}_{i}^{{prime} }left({x}_{i}^{V}+{y}_{i}^{V}right)}H{A}^{ast }}{{left(Hleft(1+{e}^{-{V}_{i}^{{prime} }left({x}_{i}^{V}+{y}_{i}^{V}right)}right)+{v}_{{max },i}{A}^{ast }right)}^{2}}-{c}_{P,i}^{V}{{P}_{i}}^{ast }$$
(13b)
The co-option of the antagonist has fundamentally changed selection on attraction (Eq. 13b vs. Equation 12a), which now balances traits affecting attraction (first term of Eq. 13b) with the costs of reduced competitive ability (second term of Eq. 13b). Co-option of the antagonist also modifies selection on defense (which is still given by Eq. 12b) by changing both trait values and equilibrium densities.Model parameterizationAll ecological parameters are estimated from empirical data. Here we parameterize the saturation constant H, maturation efficiency mi, larval mortality di, and adult mortality dA as well as the parameters for the alternative larval host plant and the alternative nectar source (see “Model validation” for other parameters).We cannot fit the saturation constant H to data because seed set saturates with even a single floral visit. We therefore estimate H as follows: D. wrightii flowers have a 91% chance of setting fruit30; thus, ({v}_{w}A/(H+{v}_{w}A))= 0.91 for a single visit (({v}_{w})A = 1). Solving (1/(H+1))= 0.91 for H yields: H = 0.1. H is assumed to be the same for D. discolor as pollination benefits saturate with a single visit for D. discolor. For maturation efficiency mi, only 0.5% of M. sexta larvae on D. wrightii survive through the final larval instar in nature34; thus, mw = 0.005. As M. sexta suffers 40% lower larval survival on D. discolor (5/8 larvae surviving to pupation) than on D. wrightii (10/10 larvae surviving to pupation) in our experiment19, we estimate that maturation efficiency is ~40% lower on D. discolor than on D. wrightii; i.e., md = (1 – 0.4)mw = 0.003. To estimate larval mortality, we note that larval survival is given by: ({m}_{i}={e}^{-{d}_{i}{D}_{i}}), where Di is development time. M. sexta has a larval stage of ~20 days on D. wrightii35 and there is no difference in development on D. wrightii and D. discolor, at least to the 5th instar19. Solving for di yields: dw ≈ 0.25 and dd ≈ 0.3. Finally, adults live ~5 days in the wild36. Assuming adult mortality is roughly the inverse of the lifespan: dA ≈ 0.2.For the alternative larval host plant, females lay similar numbers of eggs on D. wrightii and P. parviflora34; thus, visitation rate and oviposition efficiency are assumed to be the same as with D. wrightii; i.e., vp = vw and ep = ew. Because P. parviflora plants are of similar size and architecture as D. wrightii34, we assume that herbivory rate on P. parviflora is the same as on D. wrightii; i.e., hp = hw. (see “Model validation” for estimates of vw, ew, and hw). Only 1% of M. sexta larvae on P. parviflora survive through the final larval stage34; thus, mp = 0.01. As larvae have roughly the same development time on P. parviflora as on D. wrightii (~20 days37), solving ({m}_{p}={e}^{-{d}_{p}{D}_{p}}) yields an estimate of larval mortality on P. parviflora of: dp ≈ 0.25.For the alternative nectar source, A. palmeri provides M. sexta with copious amounts of nectar that females likely utilize for egg production38. M. sexta females lay 100–300 eggs/night39. If females foraging exclusively on D. wrightii lay the minimum 100 eggs/night and females that also forage at A. palmeri lay the maximum 300 eggs/night, then A. palmerii is estimated to increase oviposition by a factor of: ε = 3.Model validationPollination benefits (bi), visitation rate (vi), herbivory rate (hi), and oviposition efficiency (ei) all evolve simultaneously in the model. We independently validate the coESSs predicted by the models whenever possible by estimating these parameters using data that were not used to parameterize the models. We estimate bi via the ratio of the seed set of moth-pollinated flowers to autonomously self-pollinated flowers (autonomously self-pollinated seeds germinate as readily as do outcrossed seeds;30). Pollinated D. wrightii and D. discolor flowers set bw = 4.6 ± 0.2 and bd = 3.6 ± 0.1 times more seeds, respectively, than do autonomously self-pollinated flowers (D. wrightii: n = 21 fruit; D. discolor: n = 85 fruit). Moths averaged vw = 4.3 ± 0.6 floral visits to D. wrightii (n = 89 plants) and vd = 2.4 ± 0.4 floral visits to D. discolor (n = 33 plants) in our experiment19. Estimating the herbivory rate is very difficult in nature; however, we can make cursory estimates based on our data. A single M. sexta larvae can consume 1400–1900 cm2 of leaves, which is more than many D. wrightii plants in nature30. Assuming that an average D. wrightii plant supplies larvae with 1400 cm2 of leaves, the variation in leaf consumption (500 cm2) represents ~0.4 plants (=500/1400). Thus, M. sexta larvae are estimated to consume: hw ≈ 1 ± 0.4 D. wrightii plants. M. sexta larvae consumed roughly two times more D. discolor leaf biomass than D. wrightii leaf biomass based on our cursory estimates from our experiments; thus, hd = 2hw ≈ 2 ± 0.8. We estimate oviposition efficiency by the slope of a linear regression of the number of eggs versus the number of floral visits that each plant received from each female moth in our experiments19, which yields: ew = 0.6 ± 0.1 (n = 34 plants) and ed = 0.6 ± 0.2 (n = 24 plants) (Supplementary Data 1).Estimating evolutionary model parametersDirectly estimating evolutionary parameters with data is not possible. We therefore use theory to predict how key parameters affect eco-coevolutionary outcomes and to select reasonable parameter estimates. Our approach is as follows. We set the rates of plant and insect evolution to one (μx = μy = 1); these rates affect the speed of evolution, but not the coESSs. For each trait, we need to estimate the maximum value (bmax,i, vmax,i, hmax,i, and emax,i), the coefficient (({R}_{i}^{{prime} }), ({V}_{i}^{{prime} }), and ({H}_{i}^{{prime} })), and the associated costs (cP,iB, cP,iV, and cP,iH for plant i and cI,iV and cI,iH for the insect). Maximum trait values were chosen to constrain coevolution to a realistic range. We set the coefficients ({R}_{i}^{{prime} }), ({V}_{i}^{{prime} }), and ({H}_{i}^{{prime} }) to one for simplicity because the exact value of any trait x and y are themselves somewhat arbitrary. The costs associated with the traits therefore largely determine the coevolutionary outcomes in the model.We estimate the costs of each trait by systematically varying the costs of plant traits in the one-plant species community given reasonable values for the insect costs and then systematically varying the costs of insect traits while holding plant costs constant at their chosen values (Fig. 5). Parameter space plots show that the interactions transition from antagonism to net mutualism provided that the costs associated with insect traits underlying visitation (cI,iV) exceed a threshold below which the plant and insect engage in an evolutionary arms-race that results in the evolutionary purging of the antagonist (Fig. 5a, b). Only very rarely does the net antagonism persist. We assigned all insect traits a cost of 0.5 (black points in Fig. 5a, b) and then systematically vary the costs of plant traits associated with attraction and defense.Parameter space plots show that interactions transition from antagonism to net mutualism when the costs associated with defense are high relative to the costs associated with attraction (cP,iH > cP,iV); otherwise, coevolution drives evolutionary purging of the antagonist (Fig. 5c, d). When the costs associated with attraction and defense are both fairly high, the net antagonism persists. We assigned values of cP,iH and cP,iV to D. wrightii and D. discolor such that the parameters for D. discolor are closer to the threshold at which evolutionary purging occurs than are those of D. wrightii (Fig. 5d vs. 5c), reflecting the smaller range of ecological parameters over which M. sexta can persist with D. discolor versus with D. wrightii (Fig. 2b vs. 2a). Finally, the costs associated with pollination benefits from the antagonist (cP,iB) must be very high for the net antagonism to persist and we never observed evolutionary purging of the insect within the range of values used (see codes provided online). We assigned values of cP,iB so that pollination benefits to D. wrightii and D. discolor are well below their maximum values. Our estimates of evolutionary parameters are reported in Supplementary Table 2. Evolutionary parameters for P. parviflora are set equal to D. discolor because, in the absence of more information, both species are annual plants that may face broadly similar evolutionary constraints, at least relative to the perennial D. wrightii.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Sample collectionThe 267 samples were collected between 2009 and 2018 from 41 locations expanding from the subarctic region to the tropics (Fig. 1, Auxillary Table S1)10 and processed using the same analytical pipeline (Fig. 2). The majority of the samples were collected using a depth-discrete Limnos tube-sampler (Limnos, Poland), with the exception of the samples from La Plata reservoir (Puerto Rico), which were collected using horizontal Van Dorn sampler (5 L capacity) and samples from Lake Loclat, which were collected using a deployed PVC-inlet connected to a peristaltic pump via tubing. Of all the lakes, 29 were sampled during the open water season and the majority of the lakes were sampled once. For 12 of the lakes only surface samples taken during the ice-covered period in winter were available, and one of the Swedish lakes (Lake Lomtjärnan) was sampled twice during the ice-covered period. Moreover, a total of 5 samples (one depth profile) from the time series of the Swiss lake (Loclat) were taken from under the ice. Time series samples were taken for Lake Loclat (seven time points, Auxillary Table S1)10 and for Lake Mekkojärvi (22 time points, see Saarenheimo et al.11 for details). For most lakes and ponds, samples were collected from multiple depths, including samples from the oxic surface layer (epilimnion), the layer with steepest change in oxygen concentration and temperature (metalimnion) and from the layer where oxygen levels were below the detection limit (hypolimnion). The exception to this were the 12 Swedish lakes sampled during ice-covered period, and five shallow ponds in Canada, for which only one sample from the oxic surface layer was taken (see Auxillary Table S1)10.Fig. 2Overview of the workflow from sample collection to mOTUs.Full size imageFrom two of the lakes, Lake Lomtjärnan in Sweden and Lake Alinen Mustajärvi in Finland, samples were collected also for single cell sorting. From both locations samples were preserved in glycerol-TE (gly-TE) and from Lomtjärnan samples were preserved also using phosphate buffered saline (PBS). For both preservants, the samples were flash frozen in liquid nitrogen after first incubating for 1 minute at ambient temperature.Simultaneous to collection of the DNA samples, also samples for environmental variables were taken. Variables included temperature, pH, conductivity, oxygen, total and dissolved nutrients (P and N species), gases (CO2 or dissolved inorganic carbon and methane (CH4)), total or dissolved organic carbon, iron, sulfate and chlorophyll a (Auxillary Table S1 and Auxillary Table S210 for the methods). As the samples were collected during multiple years and by different research groups, there was some variation for the procedures between the different sampling occasions, leading to variation in the final set of environmental data across the samples.DNA extraction and metagenome sequencingMost of the DNA samples were collected on 0.2 µm Sterivex filters (Millipore), except for the time-series samples collected from Loclat, which were collected by vacuum filtration onto 47 mm polycarbonate membrane filters with 0.2 μm pore size, and time series samples from Finnish Lake Mekkojärvi, for which the water for DNA extraction was collected from epilimnion (0–0.5 m), metalimnion (0.5–1 m) and hypolimnion (1–3 m) and pooled samples from each stratum were stored in 100 ml plastic containers and frozen at −20 °C and eventually freeze-dried (Alpha 1–4 LD plus, Christ). For all filter samples, water was filtered until the filter clogged. All filters were stored frozen (−20 to −80 °C) until the extraction of DNA. For all samples, DNA was extracted using PowerSoil DNA extraction kit (MoBio, Carlsbad, CA, USA) following the manufacturer’s instructions and the DNA concentrations were measured using Qubit dsDNA HS kit (Thermo Fisher Scientific Inc.).Sequencing libraries were prepared from 10 or 20 ng of DNA using the ThruPLEX DNA-seq Prep Kit according to the manufacturer’s preparation guide. Briefly, the DNA was fragmented using a Covaris E220 system, aiming at 400 bp fragments. The ends of the fragments were end-repaired and stem-loop adapters were ligated to the 5′ ends of the fragments. The 3′ end of the stem loop were subsequently extended to close the nick. Finally, the fragments were amplified and unique index sequences were introduced using 7 cycles of PCR followed by purification using AMPure XP beads (Beckman Coulter).The quality of the libraries was evaluated using the Agilent Fragment Analyzer system and the DNF-910-kit. The adapter-ligated fragments were quantified by qPCR using the Library quantification kit for Illumina (KAPA Biosystems/Roche) on a CFX384Touch instrument (BioRad) prior to cluster generation and sequencing.The sequencing libraries were pooled and subjected to cluster generation and paired-end sequencing with 150 bp read length S2/S4 flow-cells and the NovaSeq 6000 system (Illumina Inc.) using the v1 chemistry according to the manufacturer’s protocols. Negative controls were included to the sequencing as well as 1% of PhiX control library as a positive control.Base calling was done on the instrument by RTA (v3.3.3, 3.3.5, 3.4.4) and the resulting.bcl files were demultiplexed and converted to fastq format with tools provided by Illumina Inc., allowing for one mismatch in the index sequence. Additional statistics on sequence quality were compiled with an in-house script from the fastq-files, RTA and CASAVA output files. Sequencing was performed by the SNP&SEQ Technology Platform in Uppsala, Sweden.Single-cell sorting and DNA amplificationAll Gly-TE cryopreserved samples were thawed and diluted in 1 xPBS if needed while all plates with PBS were UV-treated with a dose of 2 J prior to sorting. Samples collected from both lakes were sorted, and then screened for organisms belonging to candidate phyla radiation. Samples collected from Lake Lomtjärnan were additionally subjected to sorting based on autofluorescence to identify and sequence cells belonging to lineage Chlorobia.For obtaining SAGs from representatives of the candidate phyla radiation (CPR), samples were first stained with 1 x SYBR Green I for approximate 30 minutes. Subsequent single cell sorting was performed with a MoFlo Astrios EQ (Beckman Coulter, USA) cell sorter using a 488 nm laser for excitation, 70 µm nozzle, sheath pressure of 60 psi and 0.1 µm sterile filtered 1x PBS as sheath fluid. Individual cells were deposited into empty 384-well plates (Biorad, CA USA) UVed at 2 Joules using a CyCloneTM robotic arm and the most stringent single cell sort settings (single mode, 0.5 drop envelope). Green fluorescence (488–530/40) was used as trigger and sort decisions were made based on combined gates of 488–530/40 Height log vs 488–530/40 Area log and 488–530/40 Height log vs SSC with increasing side scatter divided up in three different regions. Flow sorting data was interpreted and displayed using the associated software Summit v 6.3.1. Next, individual cells were subject to lysis, neutralization and whole genome amplification using MDA based on the protocol and workflow described by Rinke et al.12 but with several modifications. Reagent mastermixes were added using the MANTIS liquid dispenser (Formulatrix) and the LV or HV silicone chips. The lysozyme, D2 buffer, stop solution and MDA-mastermix were each dispensed with its own chip. Most MDA-reactions were run using the phi29 from ThermoFisher but a few were run with a more heat-stable phi29, EquiPhi also provided by ThermoFisher. The MDA reaction was carried out in a total volume of 5.2 µl. Thawed, sorted cells were first pre-treated with 400 nl/well of 12 U/µl of Ready-Lyse™ Lysozyme Solution (R1804M, Lucigen) at room temperature for 15 minutes before adding 400 nl Qiagen lysis buffer D2 followed by incubation at 95 °C for 10 seconds and 10 minutes on ice. Reactions were neutralized by adding 400 nl Qiagen Stop solution. Four µl of MDA mix containing 1x reaction buffer, 0.4 mM dNTP, 0.05 mM exonuclease-resistant Hexamers, 10 mM DTT, 1.7 U phi29 DNA polymerase (ThermoFisher Scientific) and 0.5 µM Syto13 was added to a final reaction volume of 5.2 µl. All reagents except SYTO13 were UV decontaminated at 2 Joules in a UV crosslinker. The whole genome amplification was run at 30 °C for 7 or 10 h followed by an inactivation step at 65 °C for 5 min. The reaction was monitored in real time by detection of SYTO13 fluorescence every 15 minutes using a FLUOstar® Omega plate reader (BMG Labtech, Germany) or a qPCR instrument. The EquiPhi protocol was run as previously described for ThermoFisher phi29 with the following exceptions; the EquiPhi polymerase was added in 1U/reaction, reaction buffer included with the polymerase was used and the reaction was carried out at 45 °C. The single amplified genome (SAG) DNA was stored at −20 °C until further PCR screening, library preparation and Illumina sequencing.The CPR SAGs were screened using the bacterial PCR primers targeting the 16 S rRNA gene, Bact_341 F and Bact_805 R13. The reactions were run in a LightCycler 480 PCR machine (ROCHE, MA USA) in 10 µl and a final concentration of 1 x LightCycler480 SYBR Green I Master mix, 0.25 µM of each primer and 2 µl of 60 to 80 times diluted SAGs. Following a 3 min denaturation at 95 °C, targets were amplified for 40 cycles of 95 °C for 10 s, 55 °C for 20 s, 72 °C for 30 s and a final 10 min extension at 72 °C followed by melting curve analysis. The products were purified using the NucleoSpin Gel and PCR clean-up purification kit (Macherey-Nagel, Germany), quantified using the Quant-iT TM PicoGreen® dsDNA assay kit (Invitrogen, MA USA) in a FLUOstar® Omega microplate reader (BMG Labtech, Germany) and submitted for identification by Sanger sequencing at Eurofin Genomics. All SAGs were further screened using the newly designed primers targeting the phylum Parcubacteria 684F-OD1 (3′ GTAGKRRTRAAATSCGTT 5′) and 784 R (5′ TAMNVGGGTATCTAATCC -3′). These primers target with good specificity 67% of Parcubacteria in the SILVA database14. Parcu-PCR was run at 3 min at 95 °C, 40 cycles of 95 °C for 10 s, 55 °C for 20 s, 72 °C for 30 s and a final 10 min extension at 72 °C followed by melting curve analysis. The products were purified using the NucleoSpin Gel and PCR clean-up purification kit (Macherey-Nagel, Germany), quantified using the Quant-iT TM PicoGreen® dsDNA assay kit (Invitrogen, MA USA) in a FLUOstar® Omega microplate reader (BMG Labtech, Germany) and submitted for identification by Sanger sequencing at Eurofin Genomics.To recover Chlorobia single amplified genomes, sorting was done in 2016 on a MoFlo™ Astrios EQ sorter (Beckman Coulter, USA) using a 488 and 532 nm laser for excitation, a 70 μm nozzle, a sheath pressure of 60 psi, and 0.1 μm filtered 1x PBS as sheath fluid. An ND filter ND = 1 and the masks M1 and M2 were used. The trigger channel was set to the forward scatter (FSC) at a threshold of 0.025% and sort regions were defined on autofluorescence using laser 532 nm and band pass filters 710/45 and 664/22. Three populations were sorted based on differences in autofluorescence signals. The sort mode was set to single cell with a drop envelope of 0.5. The target populations were sorted at approximately 400 events per second into 96-well plates containing 1 µl 1x PBS per well with either 1 or 10 cells (positive control) deposited. A few wells remained empty (no cell sorted) were kept as negative controls. Sorted plates were stored frozen at −80 °C.The subsequent whole genome amplification was performed in 2018 using the REPLI-g Single Cell kit (QIAGEN) following the instructions provided by the manufacturer but with total reaction volume reduced to 12.5 µl. The denaturation reagent D2, stop solution, water, and reagent tubes and strips were UV-treated at 2.5 J. The lysis was changed slightly to 10 min at 65 °C, followed by 5 min on ice before adding the stop solution. To the master mix containing water, reaction buffer, and the DNA the polymerase we added SYTO 13 (Invitrogen) at a final concentration of 0.5 µM. The amplification was performed at 30 °C for 8 hours in a plate reader with fluorescence readings every 15 min. The reaction was stopped by incubating it for 5 min at 65 °C. The plate was stored for less than a week at −20 °C. Amplified DNA was mixed thoroughly by pipetting up and down 20 times before diluting it 50x and 100x in nuclease-free water. The DNA was screened for bacterial 16 S rRNA applying the primers Bact_341 F (5′- CCTACGGGNGGCWGCAG- 3′) and Bact_805 R (5′- GACTACHVGGGTATCTAATCC-3′)13 using the LightCycler® 480 SYBR Green I Master (Roche) kit. The PCR mix contained 1.5 µl diluted amplified DNA, 1x the LightCycler® 480 SYBR Green I Master mix, 0.25 µM of each primer, and nuclease-free water in a total reaction volume of 10 µl. The PCR cycling (5 min at 95 °C, followed by 40 cycles of 10 sec at 95 °C, 20 sec at 60 °C, 30 sec at 72 °C) was followed by meltcurve analysis on the LightCycler® 480 Instrument (Roche). DNA of confirmed Chlorobia was sent to sequencing as outlined below.Library preparation and Illumina sequencing of the single cellsFor the CPR-targeted analysis, Illumina libraries were prepared from sixty SAGs mainly selected from the screening procedure in a PCR-free workflow using the sparQ DNA Frag & Library Prep Kit (Quantabio) and IDT for Illumina TruSeq UD Indexes (Illumina). Libraries were prepared from 50–250 ng of MDA-products in 25% of the recommended reaction volumes according to manufacturer’s instructions. The MDA-products were fragmented for 7 minutes (5 minutes for 4 samples) without using the DNA Frag Enhancer Solution. Library insert sizes were determined using Bioanalyzer High Sensitivity DNA Kit (Agilent). Each library was quantified using the KAPA Library Quantification kit (Roche) in 5 µl reaction volumes in a 384-well plate run on LightCycler 480 (Roche) to allow equimolar pooling before sequencing on Illumina HiSeqX v2.5 PE 2 × 150 bp including negative and positive (PhiX) controls.For the Chlorobia-targeted sequencing, amplified DNA from 23 SAGs were quantified individually with Qubit dsDNA HS assay kit (ThermoFisher Scientific) and diluted to 0.2 ng/ul in nuclease free water. Sequencing libraries were prepared with Nextera XT DNA Library Preparation Kit and combinatorial combinations of molecular identifiers in the Nextera XT Index Kit (Illumina, CA USA) according to manufacturer’s instructions. Libraries with an average length of 1200 bp were quantified with Qubit dsDNA HS assay kit to allow pooling of equal amounts of the libraries based on mass. The libraries were sequenced on an Illumina MiSeq v3 PE 2 × 300 bp including negative and positive (PhiX) controls.Data processing of the metagenome and single cell sequencesThe metagenome sequencing resulted in a total of ~107 paired-end reads of length 2 × 150 bp, amounting to a total of total 3 Tbp. The raw data was trimmed using Trimmomatic (version 0.36; parameters: ILLUMINACLIP:TruSeq 3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36)15 (Auxillary Table S3)10. The trimmed data was assembled using Megahit (version 1.1.13)16 with default settings. Two types of assemblies were done, single sample assemblies for all the samples individually and a total of 53, mainly lake-wise, co-assemblies (see Auxillary Table S4)10, some samples of the Canadian ponds have also been coassembled with previously sequenced libraries of the same sample (see Auxillary Table S5)10. The relevant quality controlled reads were mapped to all the assemblies using BBmap17 with default settings and the mapping results were used to bin the contigs using Metabat (version 2.12.1, parameters –maxP 93 –minS 50 -m 1500 -s 10000)18. Genes of obtained bins were predicted and annotated using Prokka (version 1.13.3)19 using standard parameters except for the bin containing all the unbinned contigs where the –metagenome flag was used. Single-cell libraries were processed similarly to the metagenomes, but without the binning step, and using the single-cell variant of the SPAdes20 assembler instead of Megahit.Prokaryotic completeness and redundancy of all bins from Metabat and for all assembled single cells were computed using CheckM (version 1.0.13)21 (Auxillary Tables S6 and S7 for MAGs and SAGs, respectively)10. Average Nucleotide Identity (ANI) for all bin-pairs was computed with fastANI (version 1.3)22. The bins were clustered into metagenomic Operational Taxonomic Units (mOTUs) starting with 40% complete genomes with less than 5% contamination. Genome pairs with ANI above 95% were clustered into connected components. Additionally, less complete genomes were recruited to the mOTU if its ANI similarity was above 95%. Bins were taxonomically annotated in a two-step process. GTDB-Tk (version 102 with database release 89)23 was used first with default settings. Using this classification an lca database for SourMASH (version 1.0)24 was made. This database as well as one based on the GTDB release 89 was then used with SourMASH’s lca classifier for a second round of classification of bins that were not annotated with GTDB-tk (Auxillary Table S8)10.The taxonomic diversity of the bacterial (Fig. 3) and archaeal (Fig. 4) mOTUs, respectively, were visualized in a tree format. The trees were computed using GTDB-tk with one representative MAG per mOTU of the stratfreshDB, and one random representative genome per family of the GTDB. Trees were visualized using anvi’o25.Fig. 3Bacterial diversity of the stratfreshDB27. The insert illustrates the quality of the MAGs and SAGs included in the tree. Interactive version of the tree with more information available at https://anvi-server.org/moritzbuck/bacterial_diversity_of_the_stratfreshdb.Full size imageFig. 4Archaeal diversity of the stratfreshDB27. The insert illustrates the quality of the MAGs included in the tree. Interactive version of the tree with more information available at https://anvi-server.org/moritzbuck/archaeal_diversity_of_the_stratfreshdb.Full size image More

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